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Simplex Method Lab 1 Simplex Method Lab Objective: Implement the Simplex Algorithm to solve linear constrained optimization problems. The Simplex Algorithm numbers among the most important algorithms invented within the last 100 years. It provides a straightforward method for finding optimal solutions to linear constrained optimization problems. The algorithm obtains the solution by traversing the edges of the feasible region defined by the constraints. The theory of convex optimization guarantees that the optimal point will be found among the vertices of the feasible region, and so a carefully implemented Simplex Algorithm will discover the exact solution in a finite number of steps. Standard Form The Simplex Algorithm accepts a linear constrained optimization problem, also known as a linear program, in the form given below: maximize cT x subject to Ax ≤ b x ≥ 0 Note that any linear program can be converted to standard form, so there is no loss of generality in restricting our attention to this particular formulation. Such an optimization problem defines a region in space called the feasible region, the set of points satisfying the constraints. Because the constraints are all linear, the feasible region forms a geometric object called a polytope, having flat faces and edges (see Figure 1.1). The Simplex Algorithm jumps among the vertices of the feasible region searching for an optimal point. It does this by moving along the edges of the feasible region in such a way that the objective function is always increased after each move. Implementing the Simplex Algorithm is straightforward, provided one carefully follows the procedure. We will break the algorithm into several small steps, and 1 2 Lab 1. Simplex Method Figure 1.1: The feasible region for a linear program. The optimal point is one of the vertices of the polytope. write a function to perform each one. To become familiar with the execution of the Simplex algorithm, it is helpful to work several examples by hand. The Simplex Solver Our program will be more lengthy than many other lab exercises and will consist of a collection of functions working together to produce a final result. It is important to clearly define the task of each function and how all the functions will work together. If this program is written haphazardly, it will be much longer and more difficult to read than it needs to be. We will walk you through the steps of implementing the Simplex Algorithm as a Python class. For demonstration purposes, we will use the following linear program. maximize 3x0 + 2x1 subject to x0 − x1 ≤ 2 3x0 + x1 ≤ 5 4x0 + 3x1 ≤ 7 x0; x1 ≥ 0: 3 Accepting a Linear Program Our first task is to determine if we can even use the Simplex algorithm. Assuming that the problem is presented to us in standard form, we need to check that the feasible region is nonempty. Problem 1. Write a class that accepts the arrays c, A, and b of a linear optimization problem in standard form. In the constructor, check that the system is feasible at the origina. That is, check that Ax ≤ b componentwise when x = 0. Raise a ValueError if the problem is not feasible at the origin. aFor simplicity, we only check feasibility at the origin. A more robust solver sets up the auxiliary problem and solves it to find a starting point if the origin is infeasible. Adding Slack Variables Our next step is to convert the inequality constraints Ax ≤ b into equality con- straints by introducing one slack variable for each constraint. If the constraint matrix A is an m × n matrix, then there are m slack variables, one for each row of A. Grouping all of the slack variables into a vector of length m, denoted z, our constraints now take the form Ax + z = b. In our example, this gives 2 3 x2 z = 4x35 ; x4 When adding slack variables, it is useful to represent all of your variables, both the original primal variables and the additional slack variables, in a convenient manner. One effective way is to refer to a variable by its subscript. For example, we can use the integers 0 through n − 1 to refer to the original (non-slack) variables x0 through xn−1, and we can use the integers n through n+m−1 to track the slack variables (where the slack variable corresponding to the i-th row of the constraint matrix is represented by the index n + i − 1). We also need some way to track which variables are basic (non-zero) and which variables are nonbasic (those that have value 0). A useful representation for the variables is a Python list (or NumPy array), where the elements of the list are integers. Since we know how many basic variables we have (m, to be precise), we can partition the list so that all the basic variables are kept in the first m locations, and all the non-basic variables are stored at the end of the list. The ordering of this list is important. In particular, if i ≤ m, the i-th element of the list represents the basic variable corresponding to the i-th row of A. Henceforth we will refer to this list as the index list. Initially, the basic variables are simply the slack variables, and their values correspond to the values of the matrix b. In our example, we have 2 primal variables x0 and x1, and we must add 3 slack variables. Thus, we instantiate the following index list: 4 Lab 1. Simplex Method >>> L = [2, 3, 4, 0, 1] Notice how the first 3 entries of the index list are 2; 3; 4, the indices representing the slack variables. This reflects the fact that the basic variables at this point are exactly the slack variables. As the Simplex Algorithm progresses, however, the basic variables change, and it will be necessary to swap elements in our index list. Suppose the variable repre- sented by the index 4 becomes nonbasic, while the variable represented by index 0 becomes basic. We must swap these two entries in the index list. This can be done in a single, efficient line of Python code: >>> L[2], L[3] = L[3], L[2] >>> L [2, 3, 0, 4, 1] Now our index list tells us that the current basic variables have indices 2; 3; 0. Problem 2. Design and implement a way to store and track all of the basic and non-basic variables. Hint: Using integers that represent the index of each variable is useful for Problem 4. Creating a Tableau After we have determined that our program is feasible, we need to create the tableau (sometimes called the dictionary, a construct that tracks the state of the algorithm. You may structure the tableau to suit your specific implementation. Remember that your tableau will need to include in some way the slack variables that you created in Problem 2. There are many different ways to build your tableau. One way is to mimic the tableau that is often used when performing the Simplex Algorithm by hand. Define A¯ = AIm ; where Im is the m × m identity matrix, and define 2c3 607 c¯ = 6 7 : 6.7 4.5 0 That is,c ¯ 2 Rn+m such that the first n entries are c and the final m entries are zeros. Then the initial tableau has the form 0 −c¯T 1 T = : (1.1) b A¯ 0 5 The columns of the tableau correspond to each of the variables (both primal and slack), and the rows of the tableau correspond to the basic variables. Using the convention introduced above of representing the variables by indices in the index list, we have the following correspondence: column i , index i − 2; i = 2; 3; : : : ; n + m + 1; and row j , Lj−1; j = 2; 3; : : : ; m + 1; where Lj−1 refers to the (j − 1)-th entry of the index list. For our example problem, the initial index list is L = (2; 3; 4; 0; 1); and the initial tableau is 20 −3 −2 0 0 0 13 62 1 −1 1 0 0 07 T = 6 7 : 45 3 1 0 1 0 05 7 4 3 0 0 1 0 The third column corresponds to index 1, and the fourth row corresponds to index 4, since this is the third entry of the index list. The advantage of using this kind of tableau is that it is easy to check the progress of your algorithm by hand. The disadvantage is that pivot operations require careful bookkeeping to track the variables and constraints. Problem 3. Add a method to your Simplex solver that will create the initial tableau that you will use. Using a NumPy array to represent your tableau will simplify several parts of the Simplex algorithm. Pivoting Pivoting is the mechanism that really makes Simplex useful. Pivoting refers to the act of swapping basic and nonbasic variables, and transforming the tableau appro- priately. This has the effect of moving from one vertex of the feasible polytope to another vertex in a way that increases the value of the objective function. Depend- ing on how you store your variables, you may need to modify a few different parts of your solver to reflect this swapping. When initiating a pivot, you need to determine which variables will be swapped. In the tableau representation, you first find a specific element on which to pivot, and the row and column that contain the pivot element correspond to the variables that need to be swapped. Row operations are then performed on the tableau so that the pivot column becomes an elementary vector.
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